ABSTRACT Modular multilevel converter high voltage direct current (MMC‐HVDC) grids are of great significance in solving problems such as long‐distance power transmission and large‐scale renewable energy grid connection. However, their complex structure and unique operating characteristics pose challenges to transmission line protection, particularly under high‐resistance faults and noise interference. Existing methods based on single physical criteria or end‐to‐end data‐driven approaches have inherent limitations in sensitivity, reliability and interpretability. To address these issues, this paper proposes a novel collaborative protection method integrating physical mechanism guidance and attention‐based deep learning. First, a physical criterion is constructed based on voltage differential energy to achieve rapid and reliable fault section identification. Second, by introducing a gram angle difference field, the one‐dimensional voltage time‐series signal is converted into a two‐dimensional feature image that retains time correlation, effectively enhancing the expressive power of fault features. Finally, a multi‐attention convolutional neural network model is designed, which uses adaptive focusing through channel and spatial attention mechanisms to achieve accurate extraction and classification of key fault features. Simulation results show that the proposed method can achieve high‐precision fault identification of the MMC‐HVDC system under various operating conditions and has strong anti‐noise ability and high fault resistance.
Liu et al. (Thu,) studied this question.